class_docstr: The MissingValues Bar visualizer creates a bar graph that lists the total
  count of missing values for each selected feature column.
  
  When y targets are supplied to fit, the output is a stacked bar chart where
  each color corresponds to the total NaNs for the feature in that column.
  
  Parameters
  ----------
  alpha : float, default: 0.5
      A value for bending elments with the background.
  
  marker : matplotlib marker, default: |
      The marker used for each element coordinate in the plot
  
  color : string, default: black
      The color for drawing the bar chart when the y targets are not passed to
      fit.
  
  colors : list, default: None
      The color palette for drawing a stack bar chart when the y targets
      are passed to fit.
  
  classes : list, default: None
      A list of class names for the legend.
      If classes is None and a y value is passed to fit then the classes
      are selected from the target vector.
  
  kwargs : dict
      Keyword arguments that are passed to the base class and may influence
      the visualization as defined in other Visualizers.
  
  Attributes
  ----------
  features_ : np.array
      The feature labels ranked according to their importance
  
  classes_ : np.array
      The class labels for each of the target values
  
  Examples
  --------
  
  >>> from yellowbrick.contrib.missing import MissingValuesBar
  >>> visualizer = MissingValuesBar()
  >>> visualizer.fit(X, y=y)
  >>> visualizer.show()
code: def __init__(
      self, width=0.5, color=None, colors=None, classes=None, **kwargs
  ):
      if "target_type" not in kwargs:
          kwargs["target_type"] = "single"
      super(MissingValuesBar, self).__init__(**kwargs)
      self.width = width  # the width of the bars
      self.classes_ = classes
      self.ind = None
  
      # Convert to array if necessary to match estimator.classes_
      if self.classes_ is not None:
          self.classes_ = np.array(classes)
  
      # Set up classifier score visualization properties
      self.color = color
      if self.classes_ is not None:
          n_colors = len(self.classes_)
      else:
          n_colors = None
  
      self.colors = color_palette(kwargs.pop("colors", None), n_colors)
  
file_path: yellowbrick/yellowbrick/contrib/missing/bar.py
imports: import numpy
  from yellowbrick.style.palettes import color_palette
  from .base import MissingDataVisualizer
label_desc: Use image node_id 1 to create a new MissingValuesBar object from inherited base classes: MissingDataVisualizer with example: obj = MissingValuesBar(width, color, colors, classes)